metadata

license: mit
language:
  - en
tags:
  - Optimization
  - combinatorics
  - Vehicle Routing Problem
  - SVRP
  - Logistics
  - Transportation
pretty_name: SVRPBench Dataset
size_categories:
  - 10K<n<100K

🚚 SVRPBench

SVRPBench is an open and extensible benchmark for the Stochastic Vehicle Routing Problem (SVRP). It includes 500+ instances spanning small to large scales (10–1000 customers), designed to evaluate algorithms under realistic urban logistics conditions with uncertainty and operational constraints.

📌 Overview

Existing SVRP benchmarks often assume simplified, static environments, ignoring core elements of real-world routing such as time-dependent travel delays, uncertain customer availability, and dynamic disruptions. Our benchmark addresses these limitations by simulating urban logistics conditions with high fidelity:

Travel times vary based on time-of-day traffic patterns, log-normally distributed delays, and probabilistic accident occurrences
Customer time windows are sampled differently for residential and commercial clients using empirically grounded temporal distributions
A systematic dataset generation pipeline that produces diverse, constraint-rich instances including multi-depot, multi-vehicle, and capacity-constrained scenarios

📦 Dataset Components

The dataset includes various problem instances:

Problem sizes: 10, 20, 50, 100, 200, 500, 1000 customers
Variants: CVRP (Capacitated VRP), TWCVRP (Time Window Constrained VRP)
Configurations: Single/Multi-depot, Single/Multi-vehicle

Each instance includes:

Customer locations
Demand volumes
Time window constraints
Vehicle capacity limits
Depot coordinates

🧪 Supported Algorithms

The benchmark includes implementations of several algorithms:

OR-tools (Google's Operations Research tools)
ACO (Ant Colony Optimization)
Tabu Search
Nearest Neighbor with 2-opt local search
Reinforcement Learning models

📊 Benchmarking Results

Results compare algorithm performance across different problem sizes:

Model	CVRP10	CVRP20	CVRP50	CVRP100	CVRP200	CVRP500	CVRP1000
OR-tools	1.4284	1.6624	1.3793	1.1513	1.0466	0.8642	-
ACO	1.5763	1.7843	1.5120	1.2998	1.1752	1.0371	0.9254
Tabu	1.4981	1.7102	1.4578	1.2214	1.1032	0.9723	0.8735
NN+2opt	1.6832	1.8976	1.6283	1.3844	1.2627	1.1247	1.0123

🛠️ Usage

# Example of loading a dataset
from datasets import load_dataset
ds = load_dataset("MBZUAI/svrp-bench", split="test")
ds[0]

Sample

{'appear_times': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
 'demands': [0, 33, 52, 35, 85, 77, 68, 17, 61, 32, 23],
 'file_name': 'cvrp_10_multi_depot_multi_vehicule_capacities.npz',
 'instance_id': 0,
 'locations': [[523, 497],
               [394, 344],
               [536, 599],
               [341, 412],
               [734, 652],
               [492, 569],
               [491, 238],
               [419, 787],
               [688, 422],
               [708, 490],
               [431, 454]],
 'num_vehicles': 13,
 'subset_name': 'cvrp_10_multi_depot_multi_vehicule_capacities',
 'vehicle_capacities': [40.0]}

🔑 Features

Comprehensive evaluation framework for VRP algorithms
Realistic travel time modeling with time-dependent patterns
Time window constraints based on empirical distributions
Support for multi-depot and multi-vehicle scenarios
Visualization tools for solution analysis
Extensible architecture for adding new algorithms

📚 Citation

If you use this benchmark in your research, please cite:

@misc{svrbench2025,
  author = {Heakl, Ahmed and Shaaban, Yahia Salaheldin and Takáč, Martin and Lahlou, Salem and Iklassov, Zangir},
  title = {SVRPBench: A Benchmark for Stochastic Vehicle Routing Problems},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/yehias21/vrp-benchmarks}}
}

📄 License

This project is licensed under the MIT License.

📄 Paper: Arxiv